Media & Entertainment · Automated video editing
Banao builds automated video editing pipelines that detect shots, extract key moments, and package social clips and promos from long-form footage — with an editor approval step before anything goes live.
The system runs inside your MAM and CMS, not as a side tool. It handles sports highlights, news packages, interview clips, and promo reels — reducing the time from raw ingest to publishable cut.
Times Internet— AI-driven content packaging across digital video properties.
Cutting highlights is only one part of the problem. The pipeline covers ingest, scene understanding, packaging, and the handoff to your editors.
Models that parse every frame for scene boundaries, camera cuts, action peaks, and crowd-noise spikes — the raw material for every downstream clip decision.
Trained on your sport or format to identify the moments worth packaging — a goal, a key interview line, a breaking news beat — scored and ranked for editor review.
Assembles candidate clips to target durations — 15s, 30s, 60s — with intro/outro logic, title card slots, and caption drafts, ready for an editor's final approval.
Clips flow directly into your media asset management and publishing systems. No separate export queue, no manual re-ingest — the cut lands where editors already work.
Every candidate clip goes to an approval queue before publishing. Editors confirm, reject, or adjust — the AI does the first pass, not the final call.
Rejections and adjustments feed back into the model. After a few weeks of real production use, the candidate quality rises to match your house style.
Metrics shown dotted (··) are being verified for our case-study pack. Published only once confirmed.
Times Internet
Times Internet produces high volumes of short-form content for its news and entertainment platforms. Banao's automated editing pipeline detects and packages highlights from long-form footage, freeing the editorial team for the decisions that need their judgement.
Banao runs a ~300-person engineering company on its own AI — before it reaches a client. InterviewGod screens our own engineering hires every week. Vikaas runs our own demand-gen pipeline.
The standard we apply to your automated editing pipeline is the standard we hold our own internal systems to: it has to survive real operations, not just a controlled demo. That is what gets shipped.
AI does the first cut well. Here is when it does not earn its keep:
You have seen this pitched before. We start from what the problem is worth, not from a licensing brochure.
We ingest a sample of your real footage, run shot detection and highlight extraction, and hand back a candidate quality report and ROI maths — yours to keep. If you proceed, the Sprint cost is credited against the build.
Model training on your format and house style, MAM/CMS integration, approval-queue workflow, and a clip dashboard. The data pipeline and output formats are part of the deliverable.
Live deployment with editor approval workflow and feedback-loop tuning. The model sharpens weekly as your editorial team reviews and corrects candidate clips.
What formats and codecs does the pipeline support?
Banao's ingest layer handles the common broadcast and digital formats — MP4, MXF, MOV, ProRes, H.264, H.265 — and routes through your MAM's existing transcoding logic. Unusual formats are identified in the Discovery Sprint and resolved before the build starts.
Does the system need to be trained on our specific sport or show format?
Yes, and that training is part of the build. Generic highlight models exist but underperform on your format and house style. Banao trains on your labelled content, so the candidate quality matches what your editors would select.
How does the editor approval step work?
Candidate clips are presented in a review queue — your editors see the start/end frame, a confidence score, and a reason. They approve, reject, or adjust duration. Rejections feed the model; approvals publish via your CMS.
Can it handle live content, or only VOD?
Both, with different latency profiles. VOD processing runs in batch and delivers candidates within minutes of ingest. Near-live processing — match highlights ready 90 seconds post-event — is achievable but requires edge infrastructure. The Discovery Sprint scopes which applies to your use case.
We already have an NLE — does this replace it?
No. Automated editing handles the first pass: candidate clip selection and rough assembly. Your existing NLE workflow handles polish, graphics, and final sign-off. The two coexist; the AI feeds the NLE queue rather than replacing it.
Send us a sample of your hardest format — a live event, a long interview, a full match. In 45 minutes we'll show you candidate clips and the ROI maths behind building it at scale.
Book a 45-min scoping call
